TY - GEN
T1 - Trajectory-Based Reliable Content Distribution in D2D-Based Cooperative Vehicular Networks
T2 - 2018 IEEE International Conference on Communications, ICC 2018
AU - Zhou, Zhenyu
AU - Xiong, Fei
AU - Yu, Houjian
AU - Xu, Chen
AU - Mumtaz, Shahid
AU - Rodriguez, Jonathan
AU - Tariq, Muhammad
N1 - Funding Information:
This work was partially supported by the National Science Foundation of China (NSFC) under Grant Numbers 61601180, the Fundamental Research Funds for the Central Universities under Grant Numbers 2016MS17, 2017MS13; supported by Beijing Natural Science Foundation (4174104); Beijing Outstanding Young Talent under Grant Number 2016000020124G081.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - In this paper, we investigate how to achieve reliable content distribution in device-to-device (D2D) based cooperative vehicular networks by combining big data based vehicle trajectory prediction with coalition formation game based resource allocation. Firstly, vehicle trajectory is predicted based on global positioning system (GPS) and geographic information system (GIS) data, which is critical for finding reliable and longlasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay to guarantee the end-to-end quality of service (QoS), which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash- stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic.
AB - In this paper, we investigate how to achieve reliable content distribution in device-to-device (D2D) based cooperative vehicular networks by combining big data based vehicle trajectory prediction with coalition formation game based resource allocation. Firstly, vehicle trajectory is predicted based on global positioning system (GPS) and geographic information system (GIS) data, which is critical for finding reliable and longlasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay to guarantee the end-to-end quality of service (QoS), which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash- stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic.
KW - Big data
KW - Coalition formation game
KW - Cooperative vehicular networks
KW - D2D-V2V communication
KW - QoS
KW - Reliable content distribution
KW - Vehicle trajectory prediction
U2 - 10.1109/ICC.2018.8422740
DO - 10.1109/ICC.2018.8422740
M3 - Conference contribution
AN - SCOPUS:85051435405
SN - 9781538631805
T3 - IEEE International Conference on Communications
BT - 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2018 through 24 May 2018
ER -